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Record W2004433881 · doi:10.1136/ebn.6.1.19

Review: organisational change and patient directed strategies may increase adult immunisation and cancer screening

2003· letter· en· W2004433881 on OpenAlexaff
Linda Hilts

Bibliographic record

VenueEvidence-Based Nursing · 2003
Typeletter
Languageen
FieldMedicine
TopicColorectal Cancer Screening and Detection
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsMedicineWeb of sciencePsychological interventionGuidelinePediatricsGynecologyInternal medicineMeta-analysisPathologyNursing

Abstract

fetched live from OpenAlex

Stone EG, Morton SC, Hulscher ME, et al. Interventions that increase use of adult immunization and cancer screening services: a meta-analysis. Ann Intern Med2002 ; 136 : 641 –51 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] QUESTION: What is the effectiveness of interventions to increase adherence to guideline based adult immunisation and cancer screening? Studies were identified by searching (through February 1999) the Cochrane Effective Practice and Organization of Care Special Register (which includes searches of Medline [from 1966], EMBASE/Excerpta Medica [from 1980], HealthSTAR [from 1975], and the Cochrane Controlled Trials Register [from 1996]), previous systematic reviews, and the Health Care Quality Improvement Projects database. Studies were selected if they were controlled clinical trials that assessed interventions to increase the use of immunisations for influenza and pneumococcal pneumonia, and use of screening for colon, breast, and cervical cancer in adults. Data were extracted on interventions (classified as reminder, provider … [1]: {openurl}?query=rft.jtitle%253DAnnals%2Bof%2BInternal%2BMedicine%26rft.stitle%253DANN%2BINTERN%2BMED%26rft.aulast%253DStone%26rft.auinit1%253DE.%2BG.%26rft.volume%253D136%26rft.issue%253D9%26rft.spage%253D641%26rft.epage%253D651%26rft.atitle%253DInterventions%2BThat%2BIncrease%2BUse%2Bof%2BAdult%2BImmunization%2Band%2BCancer%2BScreening%2BServices%253A%2BA%2BMeta-Analysis%26rft_id%253Dinfo%253Adoi%252F10.7326%252F0003-4819-136-9-200205070-00006%26rft_id%253Dinfo%253Apmid%252F11992299%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.7326/0003-4819-136-9-200205070-00006&link_type=DOI [3]: /lookup/external-ref?access_num=11992299&link_type=MED&atom=%2Febnurs%2F6%2F1%2F19.atom [4]: /lookup/external-ref?access_num=000175357900001&link_type=ISI

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.604
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.316
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2003
Admission routes1
Has abstractyes

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